The relative merits of GEE with exchangeable correlation or GEE with independence and the sandwich estimate have been discussed, but I couldn't find a post specifically addressing my question.
I have analyzed longitudinal data using GEE with AR(1) correlation structure and the sandwich/robust estimates of the standard errors.
In the case of longitudinal data, the sandwich estimates are used to protect against miss-specifications of the correlation structure. The AR(1) was chosen based on the auto-correlation function of the data since it will produce a more efficient estimate than assuming independence. However, the univariate distrubitions of the outcomes are positively skewed and the residuals from the models exhibit some heteroscedasticity.
For GEE with longitudinal (or more generally, clustered) data, does the sandwich estimate protect against heteroscedasticity (what it was developed to do) in addition to its intended use to protect against miss-specificaiton of the correlation structure? Does reducing the heteroscedastcity with a log transformation of the outcome offer more efficiency for the estimate or some other benefit?
I would especially appreciate a reference to a journal article addressing this issue.